Open-source alternatives to Notes Linear Algebra

ML-foundations is a machine learning educational curriculum and computer science study guide. It provides a structured learning path focused on the mathematical foundations and computational prerequisites required for studying machine learning. The project serves as a Python mathematics course, delivering interactive notebooks and coding exercises to teach linear algebra, calculus, and statistics. It translates abstract mathematical formulas into concrete algorithmic code to help learners understand the principles underpinning machine learning algorithms. The curriculum covers data science p

This project is a website theme for Hugo designed to transform Markdown files into professional academic portfolios, including CVs and publication lists. It functions as a static site template and portfolio builder, enabling the creation of researcher profiles and technical blogs. The framework includes a specialized publication manager that automatically generates individual research pages and citations by importing BibTeX files or DOI references. It also features a dedicated LaTeX mathematics renderer to display technical notation and equations across the site. The system supports a broad

KaTeX is a typesetting library and web math renderer that transforms TeX and LaTeX mathematical notation into high-quality HTML and CSS for web browsers. It functions as a math notation parser and LaTeX to HTML converter, capable of operating as both a client-side library and a server-side math renderer to generate static HTML expressions. The project supports a wide range of specialized mathematical rendering, including chemical equation rendering, Bra-ket notation for quantum mechanics, and mathematical logic typesetting. It provides comprehensive controls for structural layouts such as mat

This repository is a collection of machine learning theory notes and mathematical references. It serves as a structured study guide containing conceptual explanations and handwritten mathematical derivations of the foundations and core formulas used in the field. The content focuses on the mathematical derivation of algorithms, breaking down the step-by-step logic and proofs required to understand their inner workings. These academic records utilize typesetting for precise scientific notation and mathematical documentation. The materials are organized as a markdown-based study guide with a t

Jupyter Book is a computational book publisher and static site generator that converts Jupyter notebooks and markdown files into interactive web books and publication-quality PDF documents. It serves as a markdown-based documentation tool that executes embedded code at build time and caches the resulting outputs for static display. The system distinguishes itself by supporting interactive data publications, allowing readers to engage with live computational widgets and launch notebooks in remote execution environments. It extends standard markdown with a system of roles and directives to supp

This project is a comprehensive curriculum for mastering computer science fundamentals and preparing for technical interviews. It provides over 120 interactive Python coding challenges that focus on algorithmic skill development, data structure implementation, and logical problem solving. The learning experience is delivered through a series of executable notebooks that combine instructional content with hands-on coding exercises. Each challenge is self-contained and relies on automated unit tests to verify the correctness of user-implemented solutions against predefined constraints and edge

Hedgehog Lab is a browser-based scientific computing environment designed for executing numerical analysis, matrix operations, and symbolic computation directly within a web browser. It functions as a native engine for algebraic manipulation and equation solving, allowing users to perform complex mathematical tasks without requiring external server-side infrastructure or software installations. The platform distinguishes itself by leveraging hardware acceleration to process large-scale linear algebra and matrix calculations. It integrates a symbolic engine that parses mathematical expressions

Content is a file-based content management engine that transforms Markdown and JSON files into structured data for use within web applications. It functions as a static site content engine, parsing local file systems into queryable collections while providing a library for integrating interactive components directly into text documents. The framework distinguishes itself by treating the local file hierarchy as the primary source of truth for application routing and navigation. It enables developers to embed dynamic UI elements directly into text files and provides a visual interface for editi

udlbook is a deep learning educational repository and a collection of interactive learning notebooks designed for studying neural network architectures. It serves as a digital repository of formatted mathematical equations and guided examples for learning deep learning concepts. The project provides a mathematical reference for supervised learning and neural network theory using LaTeX rendering. It includes interactive technical documentation and executable notebooks covering gradients, convolutions, and transformers. The system manages educational materials through a file-system based organ

This project is a git-based static site generator and flat-file content management system. It functions as a markdown blog engine that converts plain text files from a version-controlled repository into structured web pages. The system utilizes a git-based blogging workflow to track and manage content changes through commits and pull requests. This approach replaces traditional relational databases with flat-file content storage to manage website posts. The engine covers static site generation and markdown-based authoring to transform source text into HTML. It incorporates a git-driven deplo

PeiQi-WIKI-Book is a cybersecurity knowledge base and security research wiki. It functions as a markdown static site generator that converts structured text files into a set of interconnected HTML pages. This system serves as a curated collection of technical documentation and guides focused on vulnerability research, code auditing, and penetration testing. The project utilizes a git-driven documentation workflow, using version control hooks to automatically update a live website when content changes. It features a client-side searchable index that allows users to find security topics without

This project is a machine learning reference guide and condensed cheat sheet providing a curated collection of classical equations, diagrams, and core concepts. It serves as a technical interview study guide focused on the mathematical foundations and theoretical principles required for machine learning engineering roles. The resource facilitates the review of algorithm theory and data science interview preparation by offering a centralized location to recall fundamental machine learning patterns and mathematical proofs. It functions as a study guide for academic exams and a quick-reference t

This project is an interactive JavaScript course and beginner programming guide designed to teach fundamental scripting logic and language syntax. It functions as a web-based coding tutorial that transforms markdown-based lessons into a static site curriculum for learning web development basics. The resource features a browser-based code sandbox that allows for the execution of JavaScript snippets within a secure environment for immediate feedback. Lessons are organized into a linear sequence of modules to provide a structured onboarding process for those new to software development. The sys

This project is an interactive machine learning textbook and educational resource designed to teach the mathematical foundations of artificial intelligence. It functions as a structured course and digital book that covers essential topics ranging from basic arithmetic to advanced calculus, linear algebra, and statistics. The resource utilizes a math visualization library and a collection of interactive code examples to demonstrate abstract principles through algorithmic output. It transforms theoretical study into a practical experience by combining programmable examples with visual guides.

This repository serves as a comprehensive educational resource for machine learning, providing a structured collection of lecture notes and reference materials. It covers the fundamental mathematical and statistical principles required to build, evaluate, and optimize predictive models, ranging from basic probability and linear algebra to advanced algorithmic implementations. The content is organized through a hierarchical mapping of concepts that connects mathematical prerequisites to specific machine learning theories. It features a modular design that segments complex topics into discrete,

This is a TensorFlow learning course and machine learning education resource. It is a notebook-based interactive course that provides a deep learning tutorial series and a guide to the Keras API through executable Python code and formatted text. The material focuses on deep learning education, covering the implementation of TensorFlow models and the design of neural network architectures such as multilayer perceptrons and convolutional networks. It includes instructional content on constructing custom training loops and dataset generators for data pipeline engineering. The course covers mach

LAPACK is a comprehensive library of Fortran routines designed for high-performance numerical analysis and linear algebra. It serves as a foundational scientific computing framework, providing standardized procedures for solving systems of linear equations, eigenvalue problems, and least squares approximations. The library distinguishes itself through a hierarchical routine abstraction that organizes mathematical operations into distinct levels of complexity. It utilizes block-partitioned matrix algorithms and a column-major memory layout to optimize data locality and hardware efficiency. By

ThinkStats2 is a computational statistics course and educational library designed to teach probability and statistics through a programmatic approach. It provides a framework for studying statistical concepts by writing Python code and running simulations on real-world datasets. The project uses interactive notebooks and a collection of Python modules to deliver guided lessons. It emphasizes the verification of theoretical statistical laws through iterative computational experiments and simulation-driven testing. The resource covers broad capabilities in data analysis and data science traini

CFDPython is an educational resource for computational fluid dynamics and numerical analysis. It provides a structured curriculum to learn the physics of fluid flow by implementing numerical solutions to Navier-Stokes and partial differential equations. The project is organized as a series of incremental coding exercises delivered via Jupyter notebooks. Users build mathematical models for linear convection, diffusion, and Poisson equations across one and two dimensions to understand concepts such as convergence, stability, and numerical diffusion. The implementation utilizes NumPy for vector

Linear-Algebra-With-Python is an educational resource that provides a structured curriculum for learning linear algebra through computational practice. It serves as a tutorial for data scientists and quantitative analysts, bridging the gap between abstract mathematical theory and practical implementation using Python. The project utilizes a literate programming approach, organizing lecture notes and code examples into interactive documents. By interleaving explanatory text with functional code, it allows users to experiment with mathematical concepts directly within their development environm

This project is an educational platform designed to teach artificial intelligence, neural networks, and data science through a combination of structured textbooks and interactive learning resources. It provides a comprehensive curriculum that guides students through sequential learning paths, bridging the gap between mathematical theory and practical software implementation. The platform distinguishes itself by integrating executable code environments and dynamic browser-based visualizations directly into its educational content. These tools allow users to modify model implementations in real

This project is a comprehensive educational curriculum for learning data science and predictive modeling using the Python programming language. It provides structured instructional material and guides covering supervised learning, unsupervised learning, and neural network design. The curriculum focuses on building, training, and evaluating machine learning models. It includes specific guides for implementing linear regression, decision trees, and support vector machines for predictive analysis, as well as tutorials on designing convolutional and recurrent neural network architectures. The co

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

This project is a machine learning coursework repository containing a collection of Python exercises and notebooks. It is designed for implementing foundational machine learning algorithms and completing curriculum assignments through interactive documents that combine instructional text and executable code. The repository provides code formatted for compatibility with automated grading systems, allowing for the submission and validation of technical exercises. It includes predefined environment configurations and dependency locks to ensure consistent execution of data science tools across di

QuantumKatas is a set of quantum computing courseware and educational assets designed to teach the Q# programming language and quantum computing principles. It combines structured tutorials and coding tasks with interactive notebooks and a dedicated unit testing suite to validate the correctness of exercise implementations. The project provides a dockerized learning environment that packages all necessary tools and dependencies into a virtual image. This allows for the execution of quantum programming exercises without the need for local software installation. The curriculum covers qubit man

This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples. The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser. The content covers t

This is an interactive Python tutorial delivered as a collection of Jupyter notebooks. It is designed as a structured learning path for beginners, teaching fundamental language concepts through a sequence of lessons that combine explanatory text with runnable code cells and embedded practice exercises. Each notebook is a self-contained unit that introduces a topic, demonstrates it with a minimal code example, and then asks the learner to write code themselves, receiving immediate feedback from the browser-based execution environment. The curriculum is built on a progressive concept-stacking mo